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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kotecha, Ketan
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Computer Science & IT Engineering

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Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture Subhedar, Javed; R. Bachute, Mrinal; Kotecha, Ketan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3987-4002

Abstract

In autonomous driving systems, the semantic segmentation task involves scene partition into numerous expressive portions by classifying and labelling every image pixel for semantics. The algorithm used for semantic segmentation has a vital role in autonomous driving architecture. This paper's main contribution is optimising the semantic segmentation algorithm for autonomous driving by modifying the U-NET architecture. The optimisation techniques involve five different methods, which include; no batch normalisation network, with batch normalisation network, network with reduction in filters, average ensemble network, and weighted average ensemble network. The validation accuracy observed for the five methods were 90.28%, 91.68%, 89.80%, 92.04%, and 92.21% respectively. By reducing the filters in the network, the computation time reduces (Epoch time: 1 s 64 ms/step) as opposed to the typical (Epoch time: 4 s 260 ms/step), but the accuracy reduces. The optimisation techniques were evaluated for metrics like mean intersection over union (IoU), IoU for class, dice-metric, dice_coefficient_loss, validation loss, and accuracy. The dataset of 300 images used for this paper's study was generated using the open-source car learning to act (CARLA) simulator platform.
Towards efficient knowledge extraction: Natural language processing-based summarization of research paper introductions Chaudhari, Nikita; Vora, Deepali; Kadam, Payal; Khairnar, Vaishali; Patil, Shruti; Kotecha, Ketan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp680-691

Abstract

Academic and research papers serve as valuable platforms for disseminating expertise and discoveries to diverse audiences. The growing volume of academic papers, with nearly 7 million new publications annually, presents a formidable challenge for students and researchers alike. Consequently, the development of research paper summarization tools has become crucial to distilling crucial insights efficiently. This study examines the effectiveness of pre-trained models like text-to-text transfer transformer (T5), bidirectional encoder representations from transformers (BERT), bidirectional and auto-regressive transformer (BART), and pre-training with extracted gap-sentences for abstractive summarization (PEGASUS) on research papers, introducing a novel hybrid model merging extractive and abstractive techniques. Comparative analysis of summaries, recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU) score evaluations and author evaluation help evaluate the quality and accuracy of the generated summaries. This advancement contributes to enhancing the accessibility and efficiency of assimilating complex academic content, emphasizing the importance of advanced summarization tools in promoting the accessibility of academic knowledge.
Co-Authors Chaudhari, Nikita Kadam, Payal Khairnar, Vaishali Patil, Shruti R. Bachute, Mrinal Subhedar, Javed Vora, Deepali